Temporal network of information diffusion in Twitter

You may also like...

12 Responses

  1. Brock says:

    Great post? Any chance you can share the code that put together the graph, identified the communities, and animated this over time? Would be a huge help for us learning to apply networks in other disciplines. Thanks in advance!

  2. admin says:

    Hi Brock. Thanks for your kind comment. I am preparing the corresponding “How I did it” post. It will be ready in a couple of days. Please stay tuned

  3. Erika says:

    Awesome work! Thanks for sharing. Looking forward to your “How I did it” post. 😉

  4. admin says:

    Thank you Erika. It’s taking me some more time than I thought. Stay tuned

  5. admin says:

    Ok. It is done! Find the details of how I made the animation in this other post http://estebanmoro.org/2012/11/temporal-networks-with-igraph-and-r-with-20-lines-of-code/
    Hope you like it. Comments are welcomed

  6. Which color is for and which one is against? 🙂

  7. admin says:

    Thanks Roger for your comment: orange is in favor and dark blue is against it.

  8. John says:

    Great post! Just a quick question if you don’t mind me asking.

    I assume nodes are users and edges are tweets. Tweets “disappear” at some point, what life-span did you assume for a tweet and how did you detrmine that?

  9. admin says:

    Thanks John!
    Edges in this example are RTs. Of course retweets are instantaneous but to get a more steady version of the networks we keep the link (RT) for half an hour after it is created.

  1. November 4, 2012

    […] verloor hij me, de meer data-wijze lezers kunnen erin duiken op zijn blog, en misschien is iemand zelfs zo vriendelijk om het in gewone mensentaal hieronder in de comments […]

  2. November 9, 2012

    […] Temporal network of information diffusion in Twitter | Implicit None. […]

  3. February 6, 2013

    […] Temporal network of information diffusion in Twitter | Implicit None […]

Leave a Reply

Your email address will not be published.